The present invention pertains to the field of systems. More particularly, this invention relates to predicting system health in response to a proposed system intervention.
The operation of a wide variety of systems commonly involves system changes such as the installation of new components, the modification or reconfiguration of existing components, or the removal of existing components. For example, the administration and/or maintenance of a computer system commonly involves the installation new hardware and/or software components, the modification or reconfiguration of existing hardware and/or software components, and the removal of existing hardware and/or software components.
The components of a system commonly have dependencies with respect to one another. The software components of a computer system, for example, commonly have particular hardware requirements such as processor type or speed and/or memory requirements, etc. In addition, software components commonly have software requirements such as operating system and/or drivers, etc. Similarly, the hardware components of a computer system commonly have software and/or hardware requirements.
As the number and complexities of inter-dependencies among system components increases, so does the likelihood that system changes will significantly degrade the ability of the system to function properly. Unfortunately, prior methods for performing system changes are usually ill suited to prevent system disruptions caused by the inter-dependencies among system computers. For example, during installation of a new software component in a computer system a check us usually made, either manually or using installation software, to determine whether the required amount of disk space and/or processor, operating system, etc., requirements are satisfied. Any problems caused by more complex inter-dependencies must usually be discovered and dealt with after the system change is performed. Unfortunately, this typically leads to decreases in system performance and increased system down time.